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Classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models 7th

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Classification of Real and Deepfakes Visual Samples with Pre...

Proceedings of the 7th International Conference on Advances in computing and data sciences, ICACDS 2023

作者： Nawaz, Marriam Javed, Ali Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering UET Taxila Taxila47050 Pakistan Department of Computing Riphah International University Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Serious security and privacy problems have arisen as a result of significant advancements in the creation of deepfakes. Attackers can easily replace a person’s face with the target person’s face in an image using sophisticated Deep learning (DL) algorithms to spoof their identity. Deepfakes detection algorithms have been proposed in response to the growing concerns about the potential harm caused by deepfakes. However, a reliable deepfakes detector that can keep up with contemporary deepfakes creation techniques is required. In this work, we have proposed an end-to-end methodology for detecting manipulated visual content. We used multiple CNN models i.e., ResNet18, ResNet50, DenseNet65, DenseNet77, and DenseNet100 along with the SVM classifier to compute effective cues from the input facial faces to distinguish between actual and altered content. We have also applied the concept of transfer learning to solve the issue of model over-fitting and improve generalizability against different manipulation algorithms. A comparison study is carried out to evaluate the performance of several feature extractors. Through thorough experiments performed using the Deepfakes Detection Challenge dataset, our results demonstrated that DenseNet100 surpasses the other CNN models by better recognizing deepfakes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

Empirical Analysis of EIP-1559: Transaction Fees, Waiting Times, and Consensus Security 22

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Empirical Analysis of EIP-1559: Transaction Fees, Waiting Ti...

28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022

作者： Liu, Yulin Lu, Yuxuan Nayak, Kartik Zhang, Fan Zhang, Luyao Zhao, Yinhong SciEcon Cic London United Kingdom Center on Frontiers of Computing Studies Peking University Beijing China Department of Computer Science Duke University DurhamNC United States Department of Computer Science Yale University New HavenCT United States Data Science Research Center and Social Science Division Duke Kunshan University Suzhou China

ISBN: (纸本)9781450394505

A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs. © 2022 Owner/Author.

关键词： Blockchain

Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System

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arXiv 2024年

作者： Madduri, Ravi Li, Zilinghan Nandi, Tarak Kim, Kibaek Ryu, Minseok Rodriguez, Alex Data Science and Learning Division Argonne National Laboratory LemontIL United States Mathematics and Computer Science Argonne National Laboratory LemontIL United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

Fast point cloud generation with diffusion models in high energy physics

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Physical Review D 2023年第3期108卷 036025-036025页

作者： Vinicius Mikuni Benjamin Nachman Mariel Pettee National Energy Research Scientific Computing Center Berkeley Lab Berkeley California 94720 USA Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Berkeley Institute for Data Science University of California Berkeley California 94720 USA

Many particle physics datasets like those generated at colliders are described by continuous coordinates (in contrast to grid points like in an image), respect a number of symmetries (like permutation invariance), and have a stochastic dimensionality. For this reason, standard deep generative models that produce images or at least a fixed set of features are limiting. We introduce a new neural network simulation based on a diffusion model that addresses these limitations named fast point cloud diffusion. We show that our approach can reproduce the complex properties of hadronic jets from proton-proton collisions with competitive precision to other recently proposed models. Additionally, we use a procedure called progressive distillation to accelerate the generation time of our method, which is typically a significant challenge for diffusion models despite their state-of-the-art precision.

关键词： Particle production Artificial neural networks Machine learning

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks

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arXiv 2025年

作者： Chen, Jian Lin, Zehui Lin, Wanyu Shi, Wenlong Yin, Xiaoyan Wang, Di Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Data Science and Artificial Intelligence The Department of Computing The Hong Kong Polytechnic University Hong Kong School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Technology Northwest University Xi’an China Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia

Recently, the practical needs of "the right to be forgotten" in federated learning gave birth to a paradigm known as federated unlearning, which enables the server to forget personal data upon the client’s removal request. Existing studies on federated unlearning have primarily focused on efficiently eliminating the influence of requested data from the client’s model without retraining from scratch, however, they have rarely doubted the reliability of the global model posed by the discrepancy between its prediction performance before and after unlearning. To bridge this gap, we take the first step by introducing a novel malicious unlearning attack dubbed FedMUA, aiming to unveil potential vulnerabilities emerging from federated learning during the unlearning process. Specifically, clients may act as attackers by crafting malicious unlearning requests to manipulate the prediction behavior of the global model. The crux of FedMUA is to mislead the global model into unlearning more information associated with the influential samples for the target sample than anticipated, thus inducing adverse effects on target samples from other clients. To achieve this, we design a novel two-step method, known as Influential Sample Identification and Malicious Unlearning Generation, to identify and subsequently generate malicious feature unlearning requests within the influential samples. By doing so, we can significantly alter the predictions pertaining to the target sample by initiating the malicious feature unlearning requests, leading to the deliberate manipulation for the user adversely. Additionally, we design a new defense mechanism that is highly resilient against malicious unlearning attacks. Extensive experiments on three realistic datasets reveal that FedMUA effectively induces misclassification on target samples and can achieve an 80% attack success rate by triggering only 0.3% malicious unlearning requests. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

SACB-Net: Spatial-awareness Convolutions for Medical Image Registration

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arXiv 2025年

作者： Cheng, Xinxing Zhang, Tianyang Lu, Wenqi Meng, Qingjie Frangi, Alejandro F. Duan, Jinming School of Computer Science University of Birmingham United Kingdom Department of Computing and Mathematics Manchester Metropolitan University United Kingdom Division of Informatics Imaging and Data Sciences University of Manchester United Kingdom Centre for Computational Imaging and Modelling in Medicine University of Manchester United Kingdom Department of Computing Impeiral College London United Kingdom

Deep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds. Despite these advances, many existing approaches fall short in capturing spatially varying information in non-local regions of feature maps due to the reliance on spatially-shared convolution kernels. This limitation leads to suboptimal estimation of deformation fields. In this paper, we propose a 3D Spatial-Awareness Convolution Block (SACB) to enhance the spatial information within feature representations. Our SACB estimates the spatial clusters within feature maps by leveraging feature similarity and subsequently parameterizes the adaptive convolution kernels across diverse regions. This adaptive mechanism generates the convolution kernels (weights and biases) tailored to spatial variations, thereby enabling the network to effectively capture spatially varying information. Building on SACB, we introduce a pyramid flow estimator (named SACB-Net) that integrates SACBs to facilitate multi-scale flow composition, particularly addressing large deformations. Experimental results on the brain IXI and LPBA datasets as well as Abdomen CT datasets demonstrate the effectiveness of SACB and the superiority of SACB-Net over the state-of-the-art learning-based registration methods. The code is available at https://***/x-xc/SACB_Net. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

Large Language Models, Computational Chemistry, and Digital Reticular Chemistry: A Perspective and Proposed Workflow

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Molecular Frontiers Journal 2024年第01N02期08卷 3-6页

作者： Abdullah A. AlGhamdi Department of Chemistry and Kavli Energy Nanoscience Institute and Bakar Institute of Digital Materials for the Planet Division of Computing Data Science and Society University of California Berkeley CA USA UC Berkeley-KACST Joint Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh 11442 Saudi Arabia

In this article, I explore the synergy between Large Language Models (LLMs) and computational chemistry in the context of digital reticular chemistry and propose a workflow leveraging these technologies to advance research and discovery in the field. I argue that understanding the intricacies of new tools is imperative before integrating them into applications, and that the proposed workflow, though robust, merely offers a glimpse into the expansive potential and applications of this field.

关键词： Reticular Chemistry Digital Chemistry Computational Chemistry Large Language Models Generative Pretrained Transformer Artificial Intelligence Machine Learning data science

Robustness of graph embedding methods for community detection

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arXiv 2024年

作者： Wei, Zhi-Feng Moriano, Pablo Kannan, Ramakrishnan Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Mathematics Indiana University BloomingtonIN47405 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37830 United States

This study investigates the robustness of graph embedding methods for community detection in the face of network perturbations, specifically edge deletions. Graph embedding techniques, which represent nodes as low-dimensional vectors, are widely used for various graph machine learning tasks due to their ability to capture structural properties of networks effectively. However, the impact of perturbations on the performance of these methods remains relatively understudied. The research considers state-of-the-art graph embedding methods from two families: matrix factorization (e.g., LE, LLE, HOPE, M-NMF) and random walk-based (e.g., DeepWalk, LINE, node2vec). Through experiments conducted on both synthetic and real-world networks, the study reveals varying degrees of robustness within each family of graph embedding methods. The robustness is found to be influenced by factors such as network size, initial community partition strength, and the type of perturbation. Notably, node2vec and LLE consistently demonstrate higher robustness for community detection across different scenarios, including networks with degree and community size heterogeneity. These findings highlight the importance of selecting an appropriate graph embedding method based on the specific characteristics of the network and the task at hand, particularly in scenarios where robustness to perturbations is crucial. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph embeddings

Explainable Enrichment-Driven GrAph Reasoner (EDGAR) for Large Knowledge Graphs with Applications in Drug Repurposing

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arXiv 2024年

作者： Olasunkanmi, Olawumi Morris, Evan Kebede, Yaphet Lee, Harlin Ahalt, Stanley C. Tropsha, Alexander Bizon, Chris Department of Computer Science United States Renaissance Computing Institute United States School of Data Science and Society United States Division of Chemical Biology and Medicinal Chemistry UNC Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel HillNC United States

Knowledge graphs (KGs) represent the connections and relationships between real-world entities. We propose a link prediction framework on KGs named Enrichment-Driven GrAph Reasoner (EDGAR) that infers new edges by mining entity-local rules. This approach is based on enrichment analysis, a well-established statistical method used to calculate mechanisms common to a set of differentially expressed genes. EDGAR's inference results are inherently explainable and rankable, equipped with p-values for statistical significance of each enrichment-based rule. We demonstrate its effectiveness on a large-scale biomedical KG, ROBOKOP, focusing on drug repurposing for Alzheimer disease (AD) as a case study. Initially, we extracted 14 known drugs from the KG and identified 20 contextual biomarkers through enrichment analysis, shedding light on functional pathways relevant to the shared efficacy of drugs for AD. Subsequently, using the top 1,000 enrichment results, our enrichment-driven system identified 1,246 additional drug candidates for AD treatment. We validated the top 10 candidates using medical literature evidence. EDGAR is deployed within ROBOKOP, along with a web user interface. This is the first work to use enrichment analysis for either large graph completion or drug *** Codes 68P20 © 2024, CC BY.

关键词： Knowledge graph